scholarly journals An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

2019 ◽  
Vol 11 (11) ◽  
pp. 3085 ◽  
Author(s):  
Min Dai ◽  
Ziwei Zhang ◽  
Adriana Giret ◽  
Miguel A. Salido
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Manufacturing Industry ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Estimation Of Distribution Algorithm ◽  
Mixed Integer ◽  
Shop Scheduling ◽  
Estimation Of Distribution

Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.

scholarly journals Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 220 ◽  
Author(s):  
Tianhua Jiang ◽  
Chao Zhang ◽  
Huiqi Zhu ◽  
Jiuchun Gu ◽  
Guanlong Deng
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Whale Optimization ◽  
Energy Efficient Scheduling

Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.

scholarly journals Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Andy Ham ◽  
Myoung-Ju Park ◽  
Kyung Min Kim
Keyword(s):  
Energy Consumption ◽  
Constraint Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Computational Study ◽  
Mixed Integer ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Reducing Energy

Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.

scholarly journals A radial estimation of distribution algorithm for the job-shop scheduling problem

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Radial Function ◽  
Probability Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Probability Distributions ◽  
Estimation Of Distribution Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Estimation Of Distribution

The job-shop environment has been widely studied under different approaches. It is due to its practical characteristic that makes its research interesting. Therefore, the job-shop scheduling problem continues being attracted to develop new evolutionary algorithms. In this paper, we propose a new estimation of distribution algorithm coupled with a radial probability function. The aforementioned radial function comes from the hydrogen element. This approach is proposed in order to build a competitive evolutionary algorithm for the job-shop scheduling problem. The key point is to exploit the radial probability distribution to construct offspring, and to tackle the inconvenient of the EDAs, i.e., lack of diversity of the solutions and poor ability of exploitation. Various instances and numerical experiments are presented to illustrate, and to validate this novel research. The results, obtained from this research, permits to conclude that using radial probability distributions is an emerging field to develop new and efficient EDAs.

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